package util;

public class NeuQuant {
	protected static final int netsize = 256; /* number of colours used */

	 /* four primes near 500 - assume no image has a length so large */
	 /* that it is divisible by all four primes */
	 protected static final int prime1 = 499;
	 protected static final int prime2 = 491;
	 protected static final int prime3 = 487;
	 protected static final int prime4 = 503;

	 protected static final int minpicturebytes = (3 * prime4);
	 /* minimum size for input image */

	 /* Program Skeleton
	    ----------------
	    [select samplefac in range 1..30]
	    [read image from input file]
	    pic = (unsigned char*) malloc(3*width*height);
	    initnet(pic,3*width*height,samplefac);
	    learn();
	    unbiasnet();
	    [write output image header, using writecolourmap(f)]
	    inxbuild();
	    write output image using inxsearch(b,g,r)      */

	 /* Network Definitions
	    ------------------- */

	 protected static final int maxnetpos = (netsize - 1);
	 protected static final int netbiasshift = 4; /* bias for colour values */
	 protected static final int ncycles = 100; /* no. of learning cycles */

	 /* defs for freq and bias */
	 protected static final int intbiasshift = 16; /* bias for fractions */
	 protected static final int intbias = (((int) 1) << intbiasshift);
	 protected static final int gammashift = 10; /* gamma = 1024 */
	 protected static final int gamma = (((int) 1) << gammashift);
	 protected static final int betashift = 10;
	 protected static final int beta = (intbias >> betashift); /* beta = 1/1024 */
	 protected static final int betagamma =
	  (intbias << (gammashift - betashift));

	 /* defs for decreasing radius factor */
	 protected static final int initrad = (netsize >> 3); /* for 256 cols, radius starts */
	 protected static final int radiusbiasshift = 6; /* at 32.0 biased by 6 bits */
	 protected static final int radiusbias = (((int) 1) << radiusbiasshift);
	 protected static final int initradius = (initrad * radiusbias); /* and decreases by a */
	 protected static final int radiusdec = 30; /* factor of 1/30 each cycle */

	 /* defs for decreasing alpha factor */
	 protected static final int alphabiasshift = 10; /* alpha starts at 1.0 */
	 protected static final int initalpha = (((int) 1) << alphabiasshift);

	 protected int alphadec; /* biased by 10 bits */

	 /* radbias and alpharadbias used for radpower calculation */
	 protected static final int radbiasshift = 8;
	 protected static final int radbias = (((int) 1) << radbiasshift);
	 protected static final int alpharadbshift = (alphabiasshift + radbiasshift);
	 protected static final int alpharadbias = (((int) 1) << alpharadbshift);

	 /* Types and Global Variables
	 -------------------------- */

	 protected byte[] thepicture; /* the input image itself */
	 protected int lengthcount; /* lengthcount = H*W*3 */

	 protected int samplefac; /* sampling factor 1..30 */

	 //   typedef int pixel[4];                /* BGRc */
	 protected int[][] network; /* the network itself - [netsize][4] */

	 protected int[] netindex = new int[256];
	 /* for network lookup - really 256 */

	 protected int[] bias = new int[netsize];
	 /* bias and freq arrays for learning */
	 protected int[] freq = new int[netsize];
	 protected int[] radpower = new int[initrad];
	 /* radpower for precomputation */

	 /* Initialise network in range (0,0,0) to (255,255,255) and set parameters
	    ----------------------------------------------------------------------- */
	 public NeuQuant(byte[] thepic, int len, int sample) {

	  int i;
	  int[] p;

	  thepicture = thepic;
	  lengthcount = len;
	  samplefac = sample;

	  network = new int[netsize][];
	  for (i = 0; i < netsize; i++) {
	   network[i] = new int[4];
	   p = network[i];
	   p[0] = p[1] = p[2] = (i << (netbiasshift + 8)) / netsize;
	   freq[i] = intbias / netsize; /* 1/netsize */
	   bias[i] = 0;
	  }
	 }
	 
	 public byte[] colorMap() {
	  byte[] map = new byte[3 * netsize];
	  int[] index = new int[netsize];
	  for (int i = 0; i < netsize; i++)
	   index[network[i][3]] = i;
	  int k = 0;
	  for (int i = 0; i < netsize; i++) {
	   int j = index[i];
	   map[k++] = (byte) (network[j][0]);
	   map[k++] = (byte) (network[j][1]);
	   map[k++] = (byte) (network[j][2]);
	  }
	  return map;
	 }
	 
	 /* Insertion sort of network and building of netindex[0..255] (to do after unbias)
	    ------------------------------------------------------------------------------- */
	 public void inxbuild() {

	  int i, j, smallpos, smallval;
	  int[] p;
	  int[] q;
	  int previouscol, startpos;

	  previouscol = 0;
	  startpos = 0;
	  for (i = 0; i < netsize; i++) {
	   p = network[i];
	   smallpos = i;
	   smallval = p[1]; /* index on g */
	   /* find smallest in i..netsize-1 */
	   for (j = i + 1; j < netsize; j++) {
	    q = network[j];
	    if (q[1] < smallval) { /* index on g */
	     smallpos = j;
	     smallval = q[1]; /* index on g */
	    }
	   }
	   q = network[smallpos];
	   /* swap p (i) and q (smallpos) entries */
	   if (i != smallpos) {
	    j = q[0];
	    q[0] = p[0];
	    p[0] = j;
	    j = q[1];
	    q[1] = p[1];
	    p[1] = j;
	    j = q[2];
	    q[2] = p[2];
	    p[2] = j;
	    j = q[3];
	    q[3] = p[3];
	    p[3] = j;
	   }
	   /* smallval entry is now in position i */
	   if (smallval != previouscol) {
	    netindex[previouscol] = (startpos + i) >> 1;
	    for (j = previouscol + 1; j < smallval; j++)
	     netindex[j] = i;
	    previouscol = smallval;
	    startpos = i;
	   }
	  }
	  netindex[previouscol] = (startpos + maxnetpos) >> 1;
	  for (j = previouscol + 1; j < 256; j++)
	   netindex[j] = maxnetpos; /* really 256 */
	 }
	 
	 /* Main Learning Loop
	    ------------------ */
	 public void learn() {

	  int i, j, b, g, r;
	  int radius, rad, alpha, step, delta, samplepixels;
	  byte[] p;
	  int pix, lim;

	  if (lengthcount < minpicturebytes)
	   samplefac = 1;
	  alphadec = 30 + ((samplefac - 1) / 3);
	  p = thepicture;
	  pix = 0;
	  lim = lengthcount;
	  samplepixels = lengthcount / (3 * samplefac);
	  delta = samplepixels / ncycles;
	  alpha = initalpha;
	  radius = initradius;

	  rad = radius >> radiusbiasshift;
	  if (rad <= 1)
	   rad = 0;
	  for (i = 0; i < rad; i++)
	   radpower[i] =
	    alpha * (((rad * rad - i * i) * radbias) / (rad * rad));

	  //fprintf(stderr,"beginning 1D learning: initial radius=%d\n", rad);

	  if (lengthcount < minpicturebytes)
	   step = 3;
	  else if ((lengthcount % prime1) != 0)
	   step = 3 * prime1;
	  else {
	   if ((lengthcount % prime2) != 0)
	    step = 3 * prime2;
	   else {
	    if ((lengthcount % prime3) != 0)
	     step = 3 * prime3;
	    else
	     step = 3 * prime4;
	   }
	  }

	  i = 0;
	  while (i < samplepixels) {
	   b = (p[pix + 0] & 0xff) << netbiasshift;
	   g = (p[pix + 1] & 0xff) << netbiasshift;
	   r = (p[pix + 2] & 0xff) << netbiasshift;
	   j = contest(b, g, r);

	   altersingle(alpha, j, b, g, r);
	   if (rad != 0)
	    alterneigh(rad, j, b, g, r); /* alter neighbours */

	   pix += step;
	   if (pix >= lim)
	    pix -= lengthcount;

	   i++;
	   if (delta == 0)
	    delta = 1;
	   if (i % delta == 0) {
	    alpha -= alpha / alphadec;
	    radius -= radius / radiusdec;
	    rad = radius >> radiusbiasshift;
	    if (rad <= 1)
	     rad = 0;
	    for (j = 0; j < rad; j++)
	     radpower[j] =
	      alpha * (((rad * rad - j * j) * radbias) / (rad * rad));
	   }
	  }
	  //fprintf(stderr,"finished 1D learning: final alpha=%f !\n",((float)alpha)/initalpha);
	 }
	 
	 /* Search for BGR values 0..255 (after net is unbiased) and return colour index
	    ---------------------------------------------------------------------------- */
	 public int map(int b, int g, int r) {

	  int i, j, dist, a, bestd;
	  int[] p;
	  int best;

	  bestd = 1000; /* biggest possible dist is 256*3 */
	  best = -1;
	  i = netindex[g]; /* index on g */
	  j = i - 1; /* start at netindex[g] and work outwards */

	  while ((i < netsize) || (j >= 0)) {
	   if (i < netsize) {
	    p = network[i];
	    dist = p[1] - g; /* inx key */
	    if (dist >= bestd)
	     i = netsize; /* stop iter */
	    else {
	     i++;
	     if (dist < 0)
	      dist = -dist;
	     a = p[0] - b;
	     if (a < 0)
	      a = -a;
	     dist += a;
	     if (dist < bestd) {
	      a = p[2] - r;
	      if (a < 0)
	       a = -a;
	      dist += a;
	      if (dist < bestd) {
	       bestd = dist;
	       best = p[3];
	      }
	     }
	    }
	   }
	   if (j >= 0) {
	    p = network[j];
	    dist = g - p[1]; /* inx key - reverse dif */
	    if (dist >= bestd)
	     j = -1; /* stop iter */
	    else {
	     j--;
	     if (dist < 0)
	      dist = -dist;
	     a = p[0] - b;
	     if (a < 0)
	      a = -a;
	     dist += a;
	     if (dist < bestd) {
	      a = p[2] - r;
	      if (a < 0)
	       a = -a;
	      dist += a;
	      if (dist < bestd) {
	       bestd = dist;
	       best = p[3];
	      }
	     }
	    }
	   }
	  }
	  return (best);
	 }
	 public byte[] process() {
	  learn();
	  unbiasnet();
	  inxbuild();
	  return colorMap();
	 }
	 
	 /* Unbias network to give byte values 0..255 and record position i to prepare for sort
	    ----------------------------------------------------------------------------------- */
	 public void unbiasnet() {

	  int i;

	  for (i = 0; i < netsize; i++) {
	   network[i][0] >>= netbiasshift;
	   network[i][1] >>= netbiasshift;
	   network[i][2] >>= netbiasshift;
	   network[i][3] = i; /* record colour no */
	  }
	 }
	 
	 /* Move adjacent neurons by precomputed alpha*(1-((i-j)^2/[r]^2)) in radpower[|i-j|]
	    --------------------------------------------------------------------------------- */
	 protected void alterneigh(int rad, int i, int b, int g, int r) {

	  int j, k, lo, hi, a, m;
	  int[] p;

	  lo = i - rad;
	  if (lo < -1)
	   lo = -1;
	  hi = i + rad;
	  if (hi > netsize)
	   hi = netsize;

	  j = i + 1;
	  k = i - 1;
	  m = 1;
	  while ((j < hi) || (k > lo)) {
	   a = radpower[m++];
	   if (j < hi) {
	    p = network[j++];
	    try {
	     p[0] -= (a * (p[0] - b)) / alpharadbias;
	     p[1] -= (a * (p[1] - g)) / alpharadbias;
	     p[2] -= (a * (p[2] - r)) / alpharadbias;
	    } catch (Exception e) {
	    } // prevents 1.3 miscompilation
	   }
	   if (k > lo) {
	    p = network[k--];
	    try {
	     p[0] -= (a * (p[0] - b)) / alpharadbias;
	     p[1] -= (a * (p[1] - g)) / alpharadbias;
	     p[2] -= (a * (p[2] - r)) / alpharadbias;
	    } catch (Exception e) {
	    }
	   }
	  }
	 }
	 
	 /* Move neuron i towards biased (b,g,r) by factor alpha
	    ---------------------------------------------------- */
	 protected void altersingle(int alpha, int i, int b, int g, int r) {

	  /* alter hit neuron */
	  int[] n = network[i];
	  n[0] -= (alpha * (n[0] - b)) / initalpha;
	  n[1] -= (alpha * (n[1] - g)) / initalpha;
	  n[2] -= (alpha * (n[2] - r)) / initalpha;
	 }
	 
	 /* Search for biased BGR values
	    ---------------------------- */
	 protected int contest(int b, int g, int r) {

	  /* finds closest neuron (min dist) and updates freq */
	  /* finds best neuron (min dist-bias) and returns position */
	  /* for frequently chosen neurons, freq[i] is high and bias[i] is negative */
	  /* bias[i] = gamma*((1/netsize)-freq[i]) */

	  int i, dist, a, biasdist, betafreq;
	  int bestpos, bestbiaspos, bestd, bestbiasd;
	  int[] n;

	  bestd = ~(((int) 1) << 31);
	  bestbiasd = bestd;
	  bestpos = -1;
	  bestbiaspos = bestpos;

	  for (i = 0; i < netsize; i++) {
	   n = network[i];
	   dist = n[0] - b;
	   if (dist < 0)
	    dist = -dist;
	   a = n[1] - g;
	   if (a < 0)
	    a = -a;
	   dist += a;
	   a = n[2] - r;
	   if (a < 0)
	    a = -a;
	   dist += a;
	   if (dist < bestd) {
	    bestd = dist;
	    bestpos = i;
	   }
	   biasdist = dist - ((bias[i]) >> (intbiasshift - netbiasshift));
	   if (biasdist < bestbiasd) {
	    bestbiasd = biasdist;
	    bestbiaspos = i;
	   }
	   betafreq = (freq[i] >> betashift);
	   freq[i] -= betafreq;
	   bias[i] += (betafreq << gammashift);
	  }
	  freq[bestpos] += beta;
	  bias[bestpos] -= betagamma;
	  return (bestbiaspos);
	 }

}
